Create app.py

app.py

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+import gradio as gr
+from gradio.components import Textbox, Checkbox
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration
+from peft import PeftModel, PeftConfig
+import torch
+import datasets
+
+# Load your fine-tuned model and tokenizer
+model_name = "google/flan-t5-large"
+peft_name = "legacy107/flan-t5-large-ia3-cpgQA"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+pretrained_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large")
+model = PeftModel.from_pretrained(model, peft_name)
+
+peft_name = "legacy107/flan-t5-large-ia3-bioasq-paraphrase"
+peft_config = PeftConfig.from_pretrained(peft_name)
+paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+paraphrase_model = PeftModel.from_pretrained(paraphrase_model, peft_name)
+
+max_length = 512
+max_target_length = 200
+
+# Load your dataset
+dataset = datasets.load_dataset("minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent", split="test")
+dataset = dataset.shuffle()
+dataset = dataset.select(range(10))
+
+
+def paraphrase_answer(question, answer, use_pretrained=False):
+    # Combine question and context
+    input_text = f"question: {question}. Paraphrase the answer to make it more natural answer: {answer}"
+
+    # Tokenize the input text
+    input_ids = tokenizer(
+        input_text,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=max_length,
+    ).input_ids
+
+    # Generate the answer
+    with torch.no_grad():
+        if use_pretrained:
+            generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+        else:
+            generated_ids = paraphrase_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+
+    # Decode and return the generated answer
+    paraphrased_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    return paraphrased_answer
+
+
+# Define your function to generate answers
+def generate_answer(question, context, ground_truth, do_pretrained, do_natural, do_pretrained_natural):
+    # Combine question and context
+    input_text = f"question: {question} context: {context}"
+
+    # Tokenize the input text
+    input_ids = tokenizer(
+        input_text,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=max_length,
+    ).input_ids
+
+    # Generate the answer
+    with torch.no_grad():
+        generated_ids = model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+
+    # Decode and return the generated answer
+    generated_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    # Paraphrase answer
+    paraphrased_answer = ""
+    if do_natural:
+        paraphrased_answer = paraphrase_answer(question, generated_answer)
+
+    # Get pretrained model's answer
+    pretrained_answer = ""
+    if do_pretrained:
+        with torch.no_grad():
+            pretrained_generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+            pretrained_answer = tokenizer.decode(pretrained_generated_ids[0], skip_special_tokens=True)
+
+    # Get pretrained model's natural answer
+    pretrained_paraphrased_answer = ""
+    if do_pretrained_natural:
+        pretrained_paraphrased_answer = paraphrase_answer(question, generated_answer, True)
+
+    return generated_answer, paraphrased_answer, pretrained_answer, pretrained_paraphrased_answer
+
+
+# Define a function to list examples from the dataset
+def list_examples():
+    examples = []
+    for example in dataset:
+        context = example["context"]
+        question = example["question"]
+        answer = example["answer_text"]
+        examples.append([question, context, answer, True, True, True])
+    return examples
+
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_answer,
+    inputs=[
+        Textbox(label="Question"),
+        Textbox(label="Context"),
+        Textbox(label="Ground truth"),
+        Checkbox(label="Include pretrained model's answer"),
+        Checkbox(label="Include natural answer"),
+        Checkbox(label="Include pretrained model's natural answer")
+    ],
+    outputs=[
+        Textbox(label="Generated Answer"),
+        Textbox(label="Natural Answer"),
+        Textbox(label="Pretrained Model's Answer"),
+        Textbox(label="Pretrained Model's Natural Answer")
+    ],
+    examples=list_examples()
+)
+
+# Launch the Gradio interface
+iface.launch()